A comparison of bootstrap method and Edgeworth expansion in approximating the distribution of sample variance — one sample and two sample cases

The performance of the bootstrap method and the Edgeworth expansion in approximating the distribution of sample variance are compared when the data are from a non-normal population. Both approximations are very good. so long as the parent population is close to normal.     

Order statistics from extreme value distribution,i: tables of means,variances and covariances

Let X¹, X², …, Xⁿ be a random sample of size n from an extreme value distribution and X^1:n less than or equal X^2:n less than or equal … less than or equal X^n:n be the order statistics ob-tained from this sample. Tables of the means, variances, and covariances of the order statistics for samples of size n are given for n = 1(1)15(5)30. The computational formulae and procedure used and some checks employed are explained.     

Threshold Estimation via Group Orthogonal Greedy Algorithm

A threshold autoregressive (TAR) model is an important class of nonlinear time series models that possess many desirable features such as asymmetric limit cycles and amplitude-dependent frequencies. Statistical inference for the TAR model encounters a major difficulty in the estimation of thresholds, however. This article develops an efficient procedure to estimate the thresholds. The procedure first transforms multiple-threshold detection to a regression variable selection problem, and then employs a group orthogonal greedy algorithm to obtain the threshold estimates. Desirable theoretical results are derived to lend support to the proposed methodology. Simulation experiments are conducted to illustrate the empirical performances of the method. Applications to U.S. GNP data are investigated.     

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates

We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than the grouped LASSO and the LASSO with heredity constraints in various exploratory research scenarios encountered in epidemiological studies. We apply our method to identify genetic and non-genetic risk factors associated with smoking experimentation in a cohort of Mexican-heritage adolescents.     

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug‐drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.     

A Phase I/II adaptive design for heterogeneous groups with application to a stereotactic body radiation therapy trial

Dose‐finding studies that aim to evaluate the safety of single agents are becoming less common, and advances in clinical research have complicated the paradigm of dose finding in oncology. A class of more complex problems, such as targeted agents, combination therapies and stratification of patients by clinical or genetic characteristics, has created the need to adapt early‐phase trial design to the specific type of drug being investigated and the corresponding endpoints. In this article, we describe the implementation of an adaptive design based on a continual reassessment method for heterogeneous groups, modified to coincide with the objectives of a Phase I/II trial of stereotactic body radiation therapy in patients with painful osseous metastatic disease. Operating characteristics of the Institutional Review Board approved design are demonstrated under various possible true scenarios via simulation studies. Copyright © 2015 John Wiley & Sons, Ltd.     

INDIVIDUALS WITH INTELLECTUAL DISABILITY AND MENTAL ILLNESS: A LITERATURE REVIEW

This review highlights the urgency for a seamless and co‐ordinated approach to service delivery for people with intellectual disability and mental illness. There is emerging evidence of the high prevalence of mental illness in persons with intellectual disability. The notion that people with intellectual disability can have mental illness has only been recently acknowledged. Furthermore, people with intellectual disability also experience a wide range of psychiatric disorders often seen in the general population. Despite of recent developments in treatment options for these people, many of them continue to experience barriers in accessing mental health services.     

C市T区人民检察院适用刑事和解调研报告

在构建和谐社会与贯彻宽严相济刑事政策的背景下,各地检察机关积极探索在公诉案件中鼓励当事人和解的宽缓处理方式,取得了积极的社会效果。但由于受现行法律制度、司法资源以及传统观念的束缚,对适用和解程序处理刑事案件在案件范围、处理方式及相关配套措施等方面还存在一些问题,实践中人民检察院也有较大的顾虑,适用刑事和解的积极性不高。针对这些问题,应该从立法的层面确立刑事和解制度,明确刑事和解案件的赔偿标准,建立加害人帮教机制。     

Construction of the nearest neighbor embracing graph of a point set

This paper gives optimal algorithms for the construction of the Nearest Neighbor Embracing Graph (NNE-graph) of a given point set V of size n in the k-dimensional space (k-D) for k = 2,3. The NNE-graph provides another way of connecting points in a communication network, which has lower expected degree at each point and shorter total length of connections with respect to those using Delaunay triangulation. In fact, the NNE-graph can also be used as a tool to test whether a point set is randomly generated or has some particular properties. We show that in 2-D the NNE-graph can be constructed in optimal time in the worst case. We also present an time algorithm, where d is the -th largest degree in the utput NNE-graph. The algorithm is optimal when . The algorithm is also sensitive to the structure of the NNE-graph, for instance when , the number of edges in NNE-graph is bounded by for any value g with . We finally propose an time algorithm for the problem in 3-D, where d and are the -th largest vertex degree and the largest vertex degree in the NNE-graph, respectively. The algorithm is optimal when the largest vertex degree of the NNE-graph is .